Create README.md

README.md

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+---
+license: openrail
+datasets:
+- shareAI/ShareGPT-Chinese-English-90k
+- shareAI/CodeChat
+language:
+- zh
+- en
+library_name: transformers
+tags:
+- code
+- chat
+- codellama
+- copilot
+- codeAI
+pipeline_tag: question-answering
+---
+
+## CodeLlaMa模型的中文化版本 （支持多轮对话）
+
+科普：CodeLlaMa是专门用于代码助手的，与ChineseLlaMa不同，适用于代码类问题的回复。  
+用于多轮对话的推理代码：  
+（可以直接复制运行，默认会自动拉取该模型权重）  
+
+关联Github仓库：https://github.com/CrazyBoyM/CodeLLaMA-chat  
+
+```
+# from Firefly
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+def main():
+    model_name = 'shareAI/CodeLLaMA-chat-13b-Chinese'
+
+    device = 'cuda'
+    max_new_tokens = 500    # 每轮对话最多生成多少个token
+    history_max_len = 1000  # 模型记忆的最大token长度
+    top_p = 0.9
+    temperature = 0.35
+    repetition_penalty = 1.0
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+    ).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        use_fast=False
+    )
+
+
+    history_token_ids = torch.tensor([[]], dtype=torch.long)
+
+    user_input = input('User：')
+    while True:
+        input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids
+        eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long)
+        user_input_ids = torch.concat([input_ids, eos_token_id], dim=1)
+        history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1)
+        model_input_ids = history_token_ids[:, -history_max_len:].to(device)
+        with torch.no_grad():
+            outputs = model.generate(
+                input_ids=model_input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p,
+                temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id
+            )
+        model_input_ids_len = model_input_ids.size(1)
+        response_ids = outputs[:, model_input_ids_len:]
+        history_token_ids = torch.concat((history_token_ids, response_ids.cpu()), dim=1)
+        response = tokenizer.batch_decode(response_ids)
+        print("Bot：" + response[0].strip().replace(tokenizer.eos_token, ""))
+        user_input = input('User：')
+
+
+if __name__ == '__main__':
+    main()
+```